Low power VLSI neuron cells for artificial neural networks

Wawryn, K.; Strzeszewski, B.

doi:10.1109/iscas.1996.541611

Cited by 16 publications

(3 citation statements)

References 3 publications

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“…ANNs, on the other hand, are very amenable to hardware implementation. As a matter of fact, to date, several VLSI chips based on multilayer neural network architecture are available [8]- [9]. Also, because of the highly interconnected and coupled nature of ANNs, they are rather robust to hardware failures, since the weight is distributed among many nodes.…”

Section: Rapid Analysis and Design With Annmentioning

confidence: 99%

Design of neural networks for fast convergence and accuracy: dynamics and control

Maghami

Sparks

2000

IEEE Trans. Neural Netw.

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Section: Rapid Analysis and Design With Annmentioning

confidence: 99%

Design of neural networks for fast convergence and accuracy: dynamics and control

Maghami

Sparks

2000

IEEE Trans. Neural Netw.

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“…ANNs, on the other hand, are very amenable to hardware implementation. As a matter of fact, to date, several VLSI chips based on multilayer neural network architecture are available 7,8 .…”

Section: Training Of a Feedforward Networkmentioning

confidence: 99%

Neural networks for rapid design and analysis

Sparks

Maghami

1998

39th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference and Exhibit

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Artificial neural networks have been employed for rapid and efficient dynamics and control analysis of flexible systems.Specifically, feedforward neural networks are designed to approximate nonlinear dynamic components over prescribed input ranges, and are used in simulations as a means to speed up the overall time response analysis process. To capture the recursive nature of dynamic components with artificial neural networks, recurrent networks, which use state feedback with the appropriate number of time delays, as inputs to the networks, are employed. Once properly trained, neural networks can give very good approximations to nonlinear dynamic components, and by their judicious use in simulations, allow the analyst the potential to speed up the analysis process considerably.To illustrate this potential speed up, an existing simulation model of a spacecraft reaction wheel system is executed, first conventionally, and then with an artificial neural network in place.

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“…Thus, FPGA and VLSI designs of neural circuits and synapse networks have been implemented with conventional CMOS technology to accelerate many specific algorithms [4] [5]. In addition, the discovery of the emerging memristor device inspires the approaches of using memristors to build synapse circuit due to the similarity between the memristive and synaptic behaviors [2].…”

Section: Introductionmentioning

confidence: 99%

An EDA framework for large scale hybrid neuromorphic computing systems

Wen

et al. 2015

Proceedings of the 52nd Annual Design Automation Conference

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In implementations of neuromorphic computing systems (NCS), memristor and its crossbar topology have been widely used to realize fully connected neural networks. However, many neural networks utilized in real applications often have a sparse connectivity, which is hard to be efficiently mapped to a crossbar structure. Moreover, the scale of the neural networks is normally much larger than that can be offered by the latest integration technology of memristor crossbars. In this work, we propose AutoNCS -an EDA framework that can automate the NCS designs that combine memristor crossbars and discrete synapse modules. The connections of the neural networks are clustered to improve the utilization of the memristor elements in crossbar structures by taking into account the physical design cost of the NCS. Our results show that AutoNCS can substantially enhance the utilization efficiency of memristor crossbars while reducing the wirelength, area and delay of the physical designs of the NCS.

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Low power VLSI neuron cells for artificial neural networks

Cited by 16 publications

References 3 publications

Design of neural networks for fast convergence and accuracy: dynamics and control

Design of neural networks for fast convergence and accuracy: dynamics and control

Neural networks for rapid design and analysis

An EDA framework for large scale hybrid neuromorphic computing systems

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